Brick Kiln Dataset for Pakistan’s IGP Region Using AI

Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines low-resolution Sentinel-2 and high-resolution imagery to map brick kiln locations. Our process begins with a low-resolution analysis, followed by a post-processing step to reduce false positives, minimizing the need for extensive high-resolution imagery. This analysis initially identified 20,000 potential brick kilns, with high-resolution validation confirming around 11,000 kilns. The dataset also distinguishes between Fixed Chimney and Zigzag kilns, enabling more accurate pollution estimates for each type. Our approach demonstrates how combining satellite imagery with AI can effectively detect specific polluting sources. This dataset provides regulators with insights into brick kiln pollution, supporting interventions for unregistered kilns and actions during high pollution episodes.

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Publication Details

Journal
Scientific Data
Published
2025-05-20
DOI
https://doi.org/10.1038/s41597-025-05148-9
Citations
4
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
4.54
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article

Brick Kiln Dataset for Pakistan’s IGP Region Using AI

Maamar Hamdani, Khizer Zakir, Neetu Kushwaha, Syeda Eman Fatima et al.
4 citations
Scientific Data
Remote-Sensing Image Classification
4.54
article

Brick Kiln Dataset for Pakistan’s IGP Region Using AI

Maamar Hamdani, Khizer Zakir, Neetu Kushwaha, Syeda Eman Fatima, Hassan Aftab Sheikh
article en
4 citations

Abstract

Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines low-resolution Sentinel-2 and high-resolution imagery to map brick kiln locations. Our process begins with a low-resolution analysis, followed by a post-processing step to reduce false positives, minimizing the need for extensive high-resolution imagery. This analysis initially identified 20,000 potential brick kilns, with high-resolution validation confirming around 11,000 kilns. The dataset also distinguishes between Fixed Chimney and Zigzag kilns, enabling more accurate pollution estimates for each type. Our approach demonstrates how combining satellite imagery with AI can effectively detect specific polluting sources. This dataset provides regulators with insights into brick kiln pollution, supporting interventions for unregistered kilns and actions during high pollution episodes.

Scientific DataVol. 12(1)
University of Salzburg (AT), University of Oxford (GB), Fachhochschule Salzburg (AT), National University of Technology (PK), Pädagogische Hochschule Salzburg (AT)
Openalex Percentile: Top 5%
Remote-Sensing Image Classification
4.54
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